import math import torch import torch.nn as nn import torch.nn.functional as F from AdaptiveWingLoss.core.coord_conv import CoordConvTh def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False, dilation=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) # self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) # self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) # out = self.bn1(out) out = self.relu(out) out = self.conv2(out) # out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ConvBlock(nn.Module): def __init__(self, in_planes, out_planes): super(ConvBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.conv1 = conv3x3(in_planes, int(out_planes / 2)) self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1) self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1) if in_planes != out_planes: self.downsample = nn.Sequential( nn.BatchNorm2d(in_planes), nn.ReLU(True), nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False), ) else: self.downsample = None def forward(self, x): residual = x out1 = self.bn1(x) out1 = F.relu(out1, True) out1 = self.conv1(out1) out2 = self.bn2(out1) out2 = F.relu(out2, True) out2 = self.conv2(out2) out3 = self.bn3(out2) out3 = F.relu(out3, True) out3 = self.conv3(out3) out3 = torch.cat((out1, out2, out3), 1) if self.downsample is not None: residual = self.downsample(residual) out3 += residual return out3 class HourGlass(nn.Module): def __init__(self, num_modules, depth, num_features, first_one=False): super(HourGlass, self).__init__() self.num_modules = num_modules self.depth = depth self.features = num_features self.coordconv = CoordConvTh( x_dim=64, y_dim=64, with_r=True, with_boundary=True, in_channels=256, first_one=first_one, out_channels=256, kernel_size=1, stride=1, padding=0, ) self._generate_network(self.depth) def _generate_network(self, level): self.add_module("b1_" + str(level), ConvBlock(256, 256)) self.add_module("b2_" + str(level), ConvBlock(256, 256)) if level > 1: self._generate_network(level - 1) else: self.add_module("b2_plus_" + str(level), ConvBlock(256, 256)) self.add_module("b3_" + str(level), ConvBlock(256, 256)) def _forward(self, level, inp): # Upper branch up1 = inp up1 = self._modules["b1_" + str(level)](up1) # Lower branch low1 = F.avg_pool2d(inp, 2, stride=2) low1 = self._modules["b2_" + str(level)](low1) if level > 1: low2 = self._forward(level - 1, low1) else: low2 = low1 low2 = self._modules["b2_plus_" + str(level)](low2) low3 = low2 low3 = self._modules["b3_" + str(level)](low3) up2 = F.upsample(low3, scale_factor=2, mode="nearest") return up1 + up2 def forward(self, x, heatmap): x, last_channel = self.coordconv(x, heatmap) return self._forward(self.depth, x), last_channel class FAN(nn.Module): def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68): super(FAN, self).__init__() self.num_modules = num_modules self.gray_scale = gray_scale self.end_relu = end_relu self.num_landmarks = num_landmarks # Base part if self.gray_scale: self.conv1 = CoordConvTh( x_dim=256, y_dim=256, with_r=True, with_boundary=False, in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, ) else: self.conv1 = CoordConvTh( x_dim=256, y_dim=256, with_r=True, with_boundary=False, in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, ) self.bn1 = nn.BatchNorm2d(64) self.conv2 = ConvBlock(64, 128) self.conv3 = ConvBlock(128, 128) self.conv4 = ConvBlock(128, 256) # Stacking part for hg_module in range(self.num_modules): if hg_module == 0: first_one = True else: first_one = False self.add_module("m" + str(hg_module), HourGlass(1, 4, 256, first_one)) self.add_module("top_m_" + str(hg_module), ConvBlock(256, 256)) self.add_module("conv_last" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) self.add_module("bn_end" + str(hg_module), nn.BatchNorm2d(256)) self.add_module("l" + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0)) if hg_module < self.num_modules - 1: self.add_module("bl" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) self.add_module( "al" + str(hg_module), nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0) ) def forward(self, x): x, _ = self.conv1(x) x = F.relu(self.bn1(x), True) # x = F.relu(self.bn1(self.conv1(x)), True) x = F.avg_pool2d(self.conv2(x), 2, stride=2) x = self.conv3(x) x = self.conv4(x) previous = x outputs = [] boundary_channels = [] tmp_out = None for i in range(self.num_modules): hg, boundary_channel = self._modules["m" + str(i)](previous, tmp_out) ll = hg ll = self._modules["top_m_" + str(i)](ll) ll = F.relu(self._modules["bn_end" + str(i)](self._modules["conv_last" + str(i)](ll)), True) # Predict heatmaps tmp_out = self._modules["l" + str(i)](ll) if self.end_relu: tmp_out = F.relu(tmp_out) # HACK: Added relu outputs.append(tmp_out) boundary_channels.append(boundary_channel) if i < self.num_modules - 1: ll = self._modules["bl" + str(i)](ll) tmp_out_ = self._modules["al" + str(i)](tmp_out) previous = previous + ll + tmp_out_ return outputs, boundary_channels